Objectives

Recall race conditions: multiple threads sharing the same mutable variable without coordinating what they’re doing. This is unsafe, because the correctness of the program may depend on accidents of timing of their low-level operations.

There are basically four ways to make variable access safe in shared-memory concurrency:

Confinement. Don’t share the variable between threads. This idea is called confinement, and we’ll explore it today.

Immutability. Make the shared data immutable. We’ve talked a lot about immutability already, but there are some additional constraints for concurrent programming that we’ll talk about in this reading.

Threadsafe data type. Encapsulate the shared data in an existing threadsafe data type that does the coordination for you. We’ll talk about that today.

Synchronization. Use synchronization to keep the threads from accessing the variable at the same time. Synchronization is what you need to build your own threadsafe data type.

We’ll talk about the first three ways in this reading, along with how to make an argument that your code is threadsafe using those three ideas. We’ll talk about the fourth approach, synchronization, in the next reading.

What Threadsafe Means

A data type or static method is threadsafe if it behaves correctly when used from multiple threads, regardless of how those threads are executed, and without demanding additional coordination from the calling code.

“regardless of how threads are executed” means threads might be on multiple processors or timesliced on the same processor;

“without additional coordination” means that the data type can’t put preconditions on its caller related to timing, like “you can’t call get() while set() is in progress.”

Remember Iterator? It’s not threadsafe. Iterator’s specification says that you can’t modify a collection at the same time as you’re iterating over it. That’s a timing-related precondition put on the caller, and Iterator makes no guarantee to behave correctly if you violate it.

Strategy 1: Confinement

Our first way of achieving thread safety is confinement. Thread confinement is a simple idea: you avoid races on mutable data by keeping that data confined to a single thread. Don’t give any other threads the ability to read or write the data directly.

Since shared mutable data is the root cause of a race condition, confinement solves it by not sharing the mutable data.

Local variables are always thread confined. A local variable is stored in the stack, and each thread has its own stack. There may be multiple invocations of a method running at a time (in different threads or even at different levels of a single thread’s stack, if the method is recursive), but each of those invocations has its own private copy of the variable, so the variable itself is confined.

But be careful – the variable is thread confined, but if it’s an object reference, you also need to check the object it points to. If the object is mutable, then we want to check that the object is confined as well – there can’t be references to it that are reachable from any other thread.

Confinement is what makes the accesses to n, i, and result safe in code like this:

Let’s look at snapshot diagrams for this code.
Hover or tap on each step to update the diagram:

When we start the program, we start with one thread running main.

main creates a second thread using the anonymous Runnable idiom, and starts that thread.

At this point, we have two concurrent threads of execution.
Their interleaving is unknown!
But one possibility for the next thing that happens is that thread 1 enters computeFact.

Then, the next thing that might happen is that thread 2 also enters computeFact.

At this point, we see how confinement helps with thread safety: each execution of computeFact has its own n, i, and result variables.
None of the objects they point to are mutable; if they were mutable, we would need to check that the objects are not aliased from other threads.

Avoid Global Variables

Unlike local variables, static variables are not automatically thread confined.

If you have static variables in your program, then you have to make an argument that only one thread will ever use them, and you have to document that fact clearly. Better, you should eliminate the static variables entirely.

Here’s an example:

// This class has a race condition in it.publicclassPinballSimulator{
privatestatic PinballSimulator simulator = null;
// invariant: there should never be more than one PinballSimulator// object createdprivatePinballSimulator(){
System.out.println("created a PinballSimulator object");
}
// factory method that returns the sole PinballSimulator object,// creating it if it doesn't existpublicstatic PinballSimulator getInstance(){
if (simulator == null) {
simulator = new PinballSimulator();
}
return simulator;
}
}

This class has a race in the getInstance() method – two threads could call it at the same time and end up creating two copies of the PinballSimulator object, which we don’t want.

To fix this race using the thread confinement approach, you would specify that only a certain thread (maybe the “pinball simulation thread”) is allowed to call PinballSimulator.getInstance(). The risk here is that Java won’t help you guarantee this.

In general, static variables are very risky for concurrency. They might be hiding behind an innocuous function that seems to have no side-effects or mutations. Consider this example:

This function stores the answers from previous calls in case they’re requested again. This technique is called memoization, and it’s a sensible optimization for slow functions like exact primality testing. But now the isPrime method is not safe to call from multiple threads, and its clients may not even realize it. The reason is that the HashMap referenced by the static variable cache is shared by all calls to isPrime(), and HashMap is not threadsafe. If multiple threads mutate the map at the same time, by calling cache.put(), then the map can become corrupted in the same way that the bank account became corrupted in the last reading. If you’re lucky, the corruption may cause an exception deep in the hash map, like a Null­Pointer­Exception or Index­OutOfBounds­Exception. But it also may just quietly give wrong answers, as we saw in the bank account example.

The code has a race condition that invalidates the invariant that only one simulator object is created.

Suppose two threads are running getInstance().
One thread is about to execute one of the numbered lines above; the other thread is about to execute the other.
For each pair of possible line numbers, is it possible the invariant will be violated?

About to execute lines 1 and 3

Yes, it could be violated(missing answer)

No, we’re safe(missing answer)

(missing explanation)

About to execute lines 1 and 2

Yes, it could be violated(missing answer)

No, we’re safe(missing answer)

(missing explanation)

About to execute lines 1 and 1

Yes, it could be violated(missing answer)

No, we’re safe(missing answer)

(missing explanation)

Confinement

In the following code, which variables are confined to a single thread?

Strategy 2: Immutability

Our second way of achieving thread safety is by using immutable references and data types. Immutability tackles the shared-mutable-data cause of a race condition and solves it simply by making the shared data not mutable.

Final variables are immutable references, so a variable declared final is safe to access from multiple threads. You can only read the variable, not write it. Be careful, because this safety applies only to the variable itself, and we still have to argue that the object the variable points to is immutable.

Immutable objects are usually also threadsafe. We say “usually” here because our current definition of immutability is too loose for concurrent programming. We’ve said that a type is immutable if an object of the type always represents the same abstract value for its entire lifetime. But that actually allows the type the freedom to mutate its rep, as long as those mutations are invisible to clients. We’ve seen several examples of this notion, called beneficent mutation. Caching, lazy computation, and data structure rebalancing are typical kinds of beneficent mutation.

For concurrency, though, this kind of hidden mutation is not safe. An immutable data type that uses beneficent mutation will have to make itself threadsafe using locks (the same technique required of mutable data types), which we’ll talk about in a future reading.

Stronger definition of immutability

So in order to be confident that an immutable data type is threadsafe without locks, we need a stronger definition of immutability:

reading exercises

Suppose you’re reviewing an abstract data type which is specified to be immutable, to decide whether its implementation actually is immutable and threadsafe.

Which of the following elements would you have to look at?

fields(missing answer)

creator implementations(missing answer)

client calls to creators(missing answer)

producer implementations(missing answer)

client calls to producers(missing answer)

observer implementations(missing answer)

client calls to observers(missing answer)

mutator implementations(missing answer)

client calls to mutators(missing answer)

(missing explanation)

Strategy 3: Using Threadsafe Data Types

Our third major strategy for achieving thread safety is to store shared mutable data in existing threadsafe data types.

When a data type in the Java library is threadsafe, its documentation will explicitly state that fact. For example, here’s what StringBuffer says:

[StringBuffer is] A thread-safe, mutable sequence of characters. A string buffer is like a String, but can be modified. At any point in time it contains some particular sequence of characters, but the length and content of the sequence can be changed through certain method calls.

String buffers are safe for use by multiple threads. The methods are synchronized where necessary so that all the operations on any particular instance behave as if they occur in some serial order that is consistent with the order of the method calls made by each of the individual threads involved.

[StringBuilder is] A mutable sequence of characters. This class provides an API compatible with StringBuffer, but with no guarantee of synchronization. This class is designed for use as a drop-in replacement for StringBuffer in places where the string buffer was being used by a single thread (as is generally the case). Where possible, it is recommended that this class be used in preference to StringBuffer as it will be faster under most implementations.

It’s become common in the Java API to find two mutable data types that do the same thing, one threadsafe and the other not. The reason is what this quote indicates: threadsafe data types usually incur a performance penalty compared to an unsafe type.

It’s deeply unfortunate that StringBuffer and StringBuilder are named so similarly, without any indication in the name that thread safety is the crucial difference between them. It’s also unfortunate that they don’t share a common interface, so you can’t simply swap in one implementation for the other for the times when you need thread safety. The Java collection interfaces do much better in this respect, as we’ll see next.

Threadsafe Collections

The collection interfaces in Java – List, Set, Map – have basic implementations that are not threadsafe. The implementations of these that you’ve been used to using, namely ArrayList, HashMap, and HashSet, cannot be used safely from more than one thread.

Fortunately, just like the Collections API provides wrapper methods that make collections immutable, it provides another set of wrapper methods to make collections threadsafe, while still mutable.

These wrappers effectively make each method of the collection atomic with respect to the other methods. An atomic action effectively happens all at once – it doesn’t interleave its internal operations with those of other actions, and none of the effects of the action are visible to other threads until the entire action is complete, so it never looks partially done.

Don’t circumvent the wrapper. Make sure to throw away references to the underlying non-threadsafe collection, and access it only through the synchronized wrapper. That happens automatically in the line of code above, since the new HashMap is passed only to synchronizedMap() and never stored anywhere else. (We saw this same warning with the unmodifiable wrappers: the underlying collection is still mutable, and code with a reference to it can circumvent immutability.)

Iterators are still not threadsafe. Even though method calls on the collection itself (get(), put(), add(), etc.) are now threadsafe, iterators created from the collection are still not threadsafe. So you can’t use iterator(), or the for loop syntax:

for (String s: lst) { ... } // not threadsafe, even if lst is a synchronized list wrapper

The solution to this iteration problem will be to acquire the collection’s lock when you need to iterate over it, which we’ll talk about in a future reading.

Finally, atomic operations aren’t enough to prevent races: the way that you use the synchronized collection can still have a race condition. Consider this code, which checks whether a list has at least one element and then gets that element:

if ( ! lst.isEmpty()) { String s = lst.get(0); ... }

Even if you make lst into a synchronized list, this code still may have a race condition, because another thread may remove the element between the isEmpty() call and the get() call.

The synchronized map ensures that containsKey(), get(), and put() are now atomic, so using them from multiple threads won’t damage the rep invariant of the map. But those three operations can now interleave in arbitrary ways with each other, which might break the invariant that isPrime needs from the cache: if the cache maps an integer x to a value f, then x is prime if and only if f is true. If the cache ever fails this invariant, then we might return the wrong result.

So we have to argue that the races between containsKey(), get(), and put() don’t threaten this invariant.

The race between containsKey() and get() is not harmful because we never remove items from the cache – once it contains a result for x, it will continue to do so.

There’s a race between containsKey() and put(). As a result, it may end up that two threads will both test the primeness of the same x at the same time, and both will race to call put() with the answer. But both of them should call put() with the same answer, so it doesn’t matter which one wins the race – the result will be the same.

The need to make these kinds of careful arguments about safety – even when you’re using threadsafe data types – is the main reason that concurrency is hard.

How to Make a Safety Argument

We’ve seen that concurrency is hard to test and debug. So if you want to convince yourself and others that your concurrent program is correct, the best approach is to make an explicit argument that it’s free from races, and write it down.

A safety argument needs to catalog all the threads that exist in your module or program, and the data that that they use, and argue which of the four techniques you are using to protect against races for each data object or variable: confinement, immutability, threadsafe data types, or synchronization. When you use the last two, you also need to argue that all accesses to the data are appropriately atomic – that is, that the invariants you depend on are not threatened by interleaving. We gave one of those arguments for isPrime above.

Thread Safety Arguments for Data Types

Let’s see some examples of how to make thread safety arguments for a data type. Remember our four approaches to thread safety: confinement, immutability, threadsafe data types, and synchronization. Since we haven’t talked about synchronization in this reading, we’ll just focus on the first three approaches.

Confinement is not usually an option when we’re making an argument just about a data type, because you have to know what threads exist in the system and what objects they’ve been given access to. If the data type creates its own set of threads, then you can talk about confinement with respect to those threads. Otherwise, the threads are coming in from the outside, carrying client calls, and the data type may have no guarantees about which threads have references to what. So confinement isn’t a useful argument in that case. Usually we use confinement at a higher level, talking about the system as a whole and arguing why we don’t need thread safety for some of our modules or data types, because they won’t be shared across threads by design.

Immutability is often a useful argument:

/** MyString is an immutable data type representing a string of characters. */publicclassMyString{
privatefinalchar[] a;
// Thread safety argument:// This class is threadsafe because it's immutable:// - a is final// - a points to a mutable char array, but that array is encapsulated// in this object, not shared with any other object or exposed to a// client

Here’s another rep for MyString that requires a little more care in the argument:

Note that since this MyString rep was designed for sharing the array between multiple MyString objects, we have to ensure that the sharing doesn’t threaten its thread safety. As long as it doesn’t threaten the MyString’s immutability, however, we can be confident that it won’t threaten the thread safety.

We also have to avoid rep exposure. Rep exposure is bad for any data type, since it threatens the data type’s rep invariant. It’s also fatal to thread safety.

Why doesn’t this argument work? String is indeed immutable and threadsafe; but the rep pointing to that string, specifically the text variable, is not immutable. text is not a final variable, and in fact it can’t be final in this data type, because we need the data type to support insertion and deletion operations. So reads and writes of the text variable itself are not threadsafe. This argument is false.

Here’s another broken argument:

publicclassGraph{
privatefinal Set<Node> nodes =
Collections.synchronizedSet(new HashSet<>());
privatefinal Map<Node,Set<Node>> edges =
Collections.synchronizedMap(new HashMap<>());
// Rep invariant:// for all x, y such that y is a member of edges.get(x),// x, y are both members of nodes// Abstraction function:// represents a directed graph whose nodes are the set of nodes// and whose edges are the set (x,y) such that// y is a member of edges.get(x)// Thread safety argument:// - nodes and edges are final, so those variables are immutable// and threadsafe// - nodes and edges point to threadsafe set and map data types

This is a graph data type, which stores its nodes in a set and its edges in a map. (Quick quiz: is Graph a mutable or immutable data type? What do the final keywords have to do with its mutability?) Graph relies on other threadsafe data types to help it implement its rep – specifically the threadsafe Set and Map wrappers that we talked about above. That prevents some race conditions, but not all, because the graph’s rep invariant includes a relationship between the node set and the edge map. All nodes that appear in the edge map also have to appear in the node set. So there may be code like this:

This code has a race condition in it. There is a crucial moment when the rep invariant is violated, right after the edges map is mutated, but just before the nodes set is mutated. Another operation on the graph might interleave at that moment, discover the rep invariant broken, and return wrong results. Even though the threadsafe set and map data types guarantee that their own add() and put() methods are atomic and noninterfering, they can’t extend that guarantee to interactions between the two data structures. So the rep invariant of Graph is not safe from race conditions. Just using immutable and threadsafe-mutable data types is not sufficient when the rep invariant depends on relationships between objects in the rep.

We’ll have to fix this with synchronization, and we’ll see how in a future reading.

reading exercises

Safety arguments

Consider the following ADT with a bad safety argument that appeared above:

Which of these methods are counterexamples to the buggy safety argument, because they have a race condition?

In particular, you should mark method A as a counterexample if it’s possible that, if one thread is running method A at the same time as another thread is running some other method, some interleaving would violate A’s postcondition:

toUpperCase(missing answer)

insert(missing answer)

toString(missing answer)

clear(missing answer)

first(missing answer)

(missing explanation)

Serializability

Look again at the code for the exercise above.
We might also be concerned that clear and insert could interleave such that a client sees clear violate its postcondition.

A

B

call sb.clear()

call sb.insert(0, "a")

— in clear: text = ""

— in insert: text = "" + "a" + "z"

— clear returns

— insert returns

assert sb.toString() .equals("")

Suppose two threads are sharing MyStringBuffer sb representing "z".
They run clear and insert concurrently as shown on the right.

Thread A’s assertion will fail, but not because clear violated its postcondition.
Indeed, when all the code in clear has finished running, the postcondition is satisfied.

The real problem is that thread A has not anticipated possible interleaving between clear() and the assert.
With any threadsafe mutable type where atomic mutators are called concurrently, some mutation has to “win” by being the last one applied.
The result that thread A observed is identical to the execution below, where the mutators don’t interleave at all:

A

B

call sb.clear()

— in clear: text = ""

— clear returns

call sb.insert(0, "a")

— in insert: text = "" + "a" + ""

— insert returns

assert sb.toString() .equals("")

What we demand from a threadsafe data type is that when clients call its atomic operations concurrently, the results are consistent with some sequential ordering of the calls.
In this case, clearing and inserting, that means either clear-followed-by-insert, or insert-followed-by-clear.
This property is called serializability: for any set of operations executed concurrently, the result (the values and state observable by clients) must be a result given by some sequential ordering of those operations.

reading exercises

Serializability

Suppose two threads are sharing a MyStringBuffer representing "z".

For each pair of concurrent calls and their result, does that outcome violate serializability (and therefore demonstrate that MyStringBuffer is not threadsafe)?